Understanding State Preferences With Text As Data: Introducing the UN General Debate Corpus
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Abstract (arXiv)
Every year at the United Nations, member states deliver statements during the General Debate discussing major issues in world politics. These speeches provide invaluable information on governments' perspectives and preferences on a wide range of issues, but have largely been overlooked in the study of international politics. This paper introduces a new dataset consisting of over 7,701 English-language country statements from 1970-2016. We demonstrate how the UN General Debate Corpus (UNGDC) can be used to derive country positions on different policy dimensions using text analytic methods. The paper provides applications of these estimates, demonstrating the contribution the UNGDC can make to the study of international politics.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jul 2017.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 5; 2025: 1.
10 papers.
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- 2024Mining United Nations General Assembly Debatescited by 0
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- 2024Visualizing Temporal Topic Embeddings with a Compass16 Sep 2024
- 2024Towards Region-aware Bias Evaluation Metrics23 Jun 2024
- 2024Mining United Nations General Assembly Debates19 Jun 2024
- 2024Cognitive Insights and Stable Coalition Matching for Fostering Multi-Agent Cooperation28 May 2024
- 2024Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word Exclusion28 May 2024
- 2023Goal Driven Discovery of Distributional Differences via Language Descriptions28 Feb 2023
- 2023Neural Dynamic Focused Topic Model26 Jan 2023
- 2022Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset1 Jul 2022
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedure11 Mar 2022